Vision-based fire detection facilities work better under new deep learning model

Fast and accurate fire detection is significant to the sustainable development of human society and Earth ecology. The existence of objects with similar characteristics to fire increases the difficulty of vision-based fire detection. Improving the accuracy of fire detection by digging deeper visual features of fire always remains challenging. Recently, researchers from the Institute of Acoustics of the Chinese Academy of Sciences (IACAS) have proposed an efficient deep learning model for fast and accurate vision-based fire detection. The model is based on multiscale feature extraction, implicit deep supervision, and channel attention mechanism. The researchers utilized the real-time acquired image as the input of the model and normalized the image.